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Record W2895732082 · doi:10.1002/lary.27359

International neural monitoring study group guideline 2018 part I: Staging bilateral thyroid surgery with monitoring loss of signal

2018· review· en· W2895732082 on OpenAlexaff
Rick Schneider, Gregory W. Randolph, Gianlorenzo Dionigi, Che‐Wei Wu, Marcin Barczyński, Feng‐Yu Chiang, Zaid Al‐Quaryshi, Peter Angelos, Katrin Brauckhoff, Cláudio Roberto Cernea, John M. Chaplin, Jonathan Cheetham, Louise Davies, Peter E. Goretzki, Dana M. Hartl, Dipti Kamani, Emad Kandil, Natalia Kyriazidis, Whitney Liddy, Lisa A. Orloff, Joseph Scharpf, Jonathan W. Serpell, Jennifer J. Shin, Catherine F. Sinclair, Michael C. Singer, Susan M. Snyder, Neil Tolley, Sam Van Slycke, Erivelto Volpi, Ian Witterick, Richard J. Wong, Gayle E. Woodson, Mark Zafereo, Henning Dralle

Bibliographic record

VenueThe Laryngoscope · 2018
Typereview
Languageen
FieldMedicine
TopicThyroid and Parathyroid Surgery
Canadian institutionsMount Sinai Hospital
Fundersnot available
KeywordsMedicineGuidelineRecurrent laryngeal nerveSurgeryThyroidIntensive care medicineInternal medicinePathology

Abstract

fetched live from OpenAlex

This publication offers modern, state-of-the-art International Neural Monitoring Study Group (INMSG) guidelines based on a detailed review of the recent monitoring literature. The guidelines outline evidence-based definitions of adverse electrophysiologic events, especially loss of signal, and their incorporation in surgical strategy. These recommendations are designed to reduce technique variations, enhance the quality of neural monitoring, and assist surgeons in the clinical decision-making process involved in surgical management of recurrent laryngeal nerve. The guidelines are published in conjunction with the INMSG Guidelines Part II, Optimal Recurrent Laryngeal Nerve Management for Invasive Thyroid Cancer-Incorporation of Surgical, Laryngeal, and Neural Electrophysiologic Data. Laryngoscope, 128:S1-S17, 2018.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.007
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.002

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.075
GPT teacher head0.348
Teacher spread0.273 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations245
Published2018
Admission routes1
Has abstractyes

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